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Adaptive multipole models of optically pumped magnetometer data
Tim M Tierney1, Zelekha Seedat2, Kelly St Pier2
1Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK.
Human Brain Mapping
|March 4, 2024
Summary
Adaptive Multipole Models (AMM) offer robust interference rejection for Optically Pumped Magnetometer (OPM) data, adapting multipole expansions for diverse OPM systems and improving signal-to-noise ratio.
Area of Science:
- Neuroimaging
- Biophysics
- Signal Processing
Background:
- Multipole expansions are vital in Magnetoencephalography (MEG) for interference mitigation and brain signal modeling.
- Adapting these models for Optically Pumped Magnetometer (OPM) systems is challenging due to diverse sensor and array designs.
Purpose of the Study:
- To adapt multipole models for stable brain signal and interference modeling across various OPM systems.
- To introduce a novel method for robust interference rejection and signal enhancement in OPM data.
Main Methods:
- Utilized prolate spheroidal harmonics for compact brain signal representation on the scalp surface.
- Developed Adaptive Multipole Models (AMM) employing orthogonal projection for interference rejection.
- Compared AMM with Signal Space Separation (SSS) regarding stability, noise, and nonlinearity error robustness.
Main Results:
- Prolate spheroidal harmonics provided compact brain signal representation with as few as 100 channels.
- AMM demonstrated robust interference rejection across OPM systems, even with nonlinearity errors.
- AMM achieved up to 40 dB software shielding for visual evoked responses in a 128-channel OPM system.
Conclusions:
- AMM offers a stable and effective method for interference rejection and signal enhancement in OPM-based neuroimaging.
- AMM provides superior robustness to sensor nonlinearity errors compared to traditional SSS.
- The method successfully maximized signal-to-noise ratio in real OPM data for visual evoked responses.
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